Hugging Face has called on the US government to prioritise open-source development in its forthcoming AI Action Plan.
In a statement to the Office of Science and Technology Policy (OSTP), Hugging Face emphasised that “thoughtful policy can support innovation while ensuring that AI development remains competitive, and aligned with American values.”
Hugging Face, which hosts over 1.5 million public models across various sectors and serves seven million users, proposes an AI Action Plan centred on three interconnected pillars:
Hugging Face stresses the importance of strengthening open-source AI ecosystems. The company argues that technical innovation stems from diverse actors across institutions and that support for infrastructure – such as the National AI Research Resource (NAIRR), and investment in open science and data – allows these contributions to have an additive effect and accelerate robust innovation.
The company prioritises efficient and reliable adoption of AI. Hugging Face believes that spreading the benefits of the technology by facilitating its adoption along the value chain requires actors across sectors of activity to shape its development. It states that more efficient, modular, and robust AI models require research and infrastructural investments to enable the broadest possible participation and innovation—enabling diffusion of technology across the US economy.
Hugging Face also highlights the need to promote security and standards. The company suggests that decades of practices in open-source software cybersecurity, information security, and standards can inform safer AI technology. It advocates for promoting traceability, disclosure, and interoperability standards to foster a more resilient and robust technology ecosystem.
Open-source is key for AI advancement in the US (and beyond)
Hugging Face underlines that modern AI is built on decades of open research, with commercial giants relying heavily on open-source contributions. Recent breakthroughs – such as OLMO-2 and Olympic-Coder – demonstrate that open research remains a promising path to developing systems that match the performance of commercial models, and can often surpass them, especially in terms of efficiency and performance in specific domains.
“Perhaps most striking is the rapid compression of development timelines,” notes the company, “what once required over 100B parameter models just two years ago can now be accomplished with 2B parameter models, suggesting an accelerating path to parity.”
This trend towards more accessible, efficient, and collaborative AI development indicates that open approaches to AI development have a critical role to play in enabling a successful AI strategy that maintains technical leadership and supports more widespread and secure adoption of the technology.
Hugging Face argues that open models, infrastructure, and scientific practices constitute the foundation of AI innovation, allowing a diverse ecosystem of researchers, companies, and developers to build upon shared knowledge.
The company’s platform hosts AI models and datasets from both small actors (e.g., startups, universities) and large organisations (e.g., Microsoft, Google, OpenAI, Meta), demonstrating how open approaches accelerate progress and democratise access to AI capabilities.
“The United States must lead in open-source AI and open science, which can enhance American competitiveness by fostering a robust ecosystem of innovation and ensuring a healthy balance of competition and shared innovation,” states Hugging Face.
Research has shown that open technical systems act as force multipliers for economic impact, with an estimated 2000x multiplier effect. This means that $4 billion invested in open systems could potentially generate $8 trillion in value for companies using them.
These economic benefits extend to national economies as well. Without any open-source software contributions, the average country would lose 2.2% of its GDP. Open-source drove between €65 billion and €95 billion of European GDP in 2018 alone, a finding so significant that the European Commission cited it when establishing new rules to streamline the process for open-sourcing government software.
This demonstrates how open-source impact translates directly into policy action and economic advantage at the national level, underlining the importance of open-source as a public good.
Practical factors driving commercial adoption of open-source AI
Hugging Face identifies several practical factors driving the commercial adoption of open models:
Cost efficiency is a major driver, as developing AI models from scratch requires significant investment, so leveraging open foundations reduces R&D expenses.
Customisation is crucial, as organisations can adapt and deploy models specifically tailored to their use cases rather than relying on one-size-fits-all solutions.
Open models reduce vendor lock-in, giving companies greater control over their technology stack and independence from single providers.
Open models have caught up to and, in certain cases, surpassed the capabilities of closed, proprietary systems.
These factors are particularly valuable for startups and mid-sized companies, which can access cutting-edge technology without massive infrastructure investments. Banks, pharmaceutical companies, and other industries have been adapting open models to specific market needs—demonstrating how open-source foundations support a vibrant commercial ecosystem across the value chain.
Hugging Face’s policy recommendations to support open-source AI in the US
To support the development and adoption of open AI systems, Hugging Face offers several policy recommendations:
Enhance research infrastructure: Fully implement and expand the National AI Research Resource (NAIRR) pilot. Hugging Face’s active participation in the NAIRR pilot has demonstrated the value of providing researchers with access to computing resources, datasets, and collaborative tools.
Allocate public computing resources for open-source: The public should have ways to participate via public AI infrastructure. One way to do this would be to dedicate a portion of publicly-funded computing infrastructure to support open-source AI projects, reducing barriers to innovation for smaller research teams and companies that cannot afford proprietary systems.
Enable access to data for developing open systems: Create sustainable data ecosystems through targeted policies that address the decreasing data commons. Publishers are increasingly signing data licensing deals with proprietary AI model developers, meaning that quality data acquisition costs are now approaching or even surpassing computational expenses of training frontier models, threatening to lock out small open developers from access to quality data. Support organisations that contribute to public data repositories and streamline compliance pathways that reduce legal barriers to responsible data sharing.
Develop open datasets: Invest in the creation, curation, and maintenance of robust, representative datasets that can support the next generation of AI research and applications. Expand initiatives like the IBM AI Alliance Trusted Data Catalog and support projects like IDI’s AI-driven Digitization of the public collections in the Boston Public Library.
Strengthen rights-respecting data access frameworks: Establish clear guidelines for data usage, including standardised protocols for anonymisation, consent management, and usage tracking. Support public-private partnerships to create specialised data trusts for high-value domains like healthcare and climate science, ensuring that individuals and organisations maintain appropriate control over their data while enabling innovation.
Invest in stakeholder-driven innovation: Create and support programmes that enable organisations across diverse sectors (healthcare, manufacturing, education) to develop customised AI systems for their specific needs, rather than relying exclusively on general-purpose systems from major providers. This enables broader participation in the AI ecosystem and ensures that the benefits of AI extend throughout the economy.
Strengthen centres of excellence: Expand NIST’s role as a convener for AI experts across academia, industry, and government to share lessons and develop best practices. In particular, the AI Risk Management Framework has played a significant role in identifying stages of AI development and research questions that are critical to ensuring more robust and secure technology deployment for all. The tools developed at Hugging Face, from model documentation to evaluation libraries, are directly shaped by these questions.
Support high-quality data for performance and reliability evaluation: AI development depends heavily on data, both to train models and to reliably evaluate their progress, strengths, risks, and limitations. Fostering greater access to public data in a safe and secure way and ensuring that the evaluation data used to characterise models is sound and evidence-based will accelerate progress in both performance and reliability of the technology.
Prioritising efficient and reliable AI adoption
Hugging Face highlights that smaller companies and startups face significant barriers to AI adoption due to high costs and limited resources. According to IDC, global AI spending will reach $632 billion in 2028, but these costs remain prohibitive for many small organisations.
For organisations adopting open-source AI tools, it brings financial returns. 51% of surveyed companies currently utilising open-source AI tools report positive ROI, compared to just 41% of those not using open-source.
However, energy scarcity presents a growing concern, with the International Energy Agency projecting that data centres’ electricity consumption could double from 2022 levels to 1,000 TWh by 2026, equivalent to Japan’s entire electricity demand. While training AI models is energy-intensive, inference, due to its scale and frequency, can ultimately exceed training energy consumption.
Ensuring broad AI accessibility requires both hardware optimisations and scalable software frameworks. A range of organisations are developing models tailored to their specific needs, and US leadership in efficiency-focused AI development presents a strategic advantage. The DOE’s AI for Energy initiative further supports research into energy-efficient AI, facilitating wider adoption without excessive computational demands.
With its letter to the OSTP, Hugging Face advocates for an AI Action Plan centred on open-source principles. By taking decisive action, the US can secure its leadership, drive innovation, enhance security, and ensure the widespread benefits of AI are realised across society and the economy.
See also: UK minister in US to pitch Britain as global AI investment hub
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